Weekly AI Update The AI chip gold rush has a new banker worth $2.4B Volta Infra, a brand new AI cloud startup, just raised $300 million plus $5 billion in financing to help more companies afford Nvidia's pricey chips, not just the tech giants. Backed by Nvidia, Michael Dell, Andreessen Horowitz, and Altimeter, it has already locked in a $10 billion contract to supply cloud capacity to an unnamed AI developer over six years. The bet is that whoever solves AI's financing problem wins, though critics warn of risky circular deals and a coming shakeout. Google's $1.5B play for Mechanize could finally fix its coding AI problem Google is reportedly in talks to pay around $1.5 billion to license technology from Mechanize, a young AI startup building simulated environments and benchmarks to train coding agents, while also hiring some of its top evaluation experts. The move fits a familiar Google playbook of striking talent and licensing deals that sidestep antitrust scrutiny, much like its earlier tie-ups with Windsurf and The goal is clear: close the gap with Anthropic's Claude Code and OpenAI's Codex, which have pulled ahead in the fast growing market for AI coding tools. Anthropic wants to build its own chips to run Claude faster and cheaper Anthropic confirmed it is assembling a custom silicon team to design its own AI chips, hiring engineers who will develop hardware and models together so Claude can run faster and at greater scale. The move puts it alongside OpenAI, Google, and Meta, all of which have pushed into custom chips, though building an advanced one can cost close to half a billion dollars. It is the latest piece of a massive infrastructure push that also includes a $15 billion Texas data center campus and deals with $AMZN, $NVDA, $AMD, and Samsung.

A new survey suggests that most Steam users are okay with AI in games. > 43% said they’re completely fine with AI in games. > 25.6% said they’re neutral. > 31.4% said they have a negative view of AI in games. >8.1% said they would never play a game that uses AI. The survey, conducted by GameDiscoverCo with around 3,800 Steam users, also found that players are generally more comfortable with AI being used for things like coding AI-generated art, voice acting, and story content were much more controversial.

🌍 Happy Labour Day from Audiera 💜 Today, we celebrate the passion, creativity, and dedication of every builder, creator, dancer, and dreamer shaping the future of Web3. At Audiera, we believe every move matters — whether you're building communities, creating content, coding AI, or dancing to the beat of innovation. 🎶✨ Take a break, enjoy the rhythm, and keep vibing with us. The future of AI + Social + Gaming is built together. Powered by AI idols @Kira_0x4154 & @Ray_0x4245. Start your Ai journey: ▶️ 📷 #LabourDay# #HappyLabourDay# #Audiera# #Web3Gaming# #AIagents# #TelegramMiniApp# #DanceToEarn#

🔬 Can AI actually "discover" science? Tested on 90 real Nature papers, even the strongest agent beat SOTA on under 20% of tasks. Title: NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers? URL: 📋 Overview Using 90 tasks drawn from Nature-family journals across six domains, this work tests whether AI coding agents can reproduce and surpass published SOTA. An "information firewall" hides the original method, and NatureGym builds per-task containerized environments for strict, unified evaluation with web search disabled. 🎯 The problem it solves Prior agent-on-research benchmarks were fragmented and low in credibility. This work forces genuine discovery instead of reproduction and creates a common ground for cross-domain comparison. 📐 Methodology Tasks are scored with a SOTA-normalized relative gap (g > 0.1 to surpass, g >= 0 to match), comparing across 81 metrics. Ten agents are evaluated across three harnesses under a 4-hour budget with task-based GPU allocation. 📊 Results ・Even the top agent, Claude Opus 4.7, surpassed SOTA on only 17.8% of tasks and matched on 47.8% ・45.5% of successes were just translating a science task into supervised prediction; genuine domain reasoning was only 8.3% ・Failures were driven by wrong method choice (45.1%) and insufficient compute (24.4%), with task misunderstanding at only 3.1% ・Interdisciplinary tasks consistently scored worse A sober snapshot: today's agents are good at "translation" but weak at real "discovery." #AI4Science# #CodingAgents#

Coding agents fire dozens of API calls per task, so a single developer can quietly burn thousands of dollars a week before anyone notices — here's how LangChain killed that "spend unpredictability" internally 💸 The key was folding budget control into the same place as observability. Title: How LangChain Made Coding Agent Spend Predictable URL: 💸 Overview An LLM Gateway built into LangSmith gives a company-wide, minute-level view of model spend and manages budgets centrally. Rather than a bolt-on proxy, it sits on the same foundation as existing tracing, evaluation, and user management. ❓ Challenges Solved Model usage spread from a few teams to the whole company, and premium model prices rose, so costs spiked. ・Coding agents trigger dozens of API calls per task ・Individual developers ran up thousands of dollars a week, unnoticed until month-end 💡 Methodology & Proposed Approach Budgets can be set across multiple layers. ・Caps at the organization, workspace, user, and API-key level ・Default monthly, weekly, daily, and hourly windows for all employees, with exceptions for heavy projects ・Covers agents accessed via Claude Code, Codex, and LangChain Deep Agents ・Deployed via MDM so no one has to set it up manually ・Runs are traced and tied to a user and API key; overspend can be diagnosed by inspecting the trace with evaluation data 🌍 Use Cases Engineering leaders can set team-level limits while still letting people use agents without fear of a surprise bill. The practical value is replacing the month-end billing shock with real-time monitoring. 📊 Lessons & Outcomes ・Static price tables go stale fast, so pricing must be handled dynamically, including caching and tier differences ・Cursor and Claude Desktop didn't route cleanly, so they measured the delta between Gateway-captured traffic and provider settings to correct for it ・Hard limits alone block real work, so they evolved into early-warning alerts and auditable budget-increase requests ・Since internal rollout, LLM costs have stayed within budget #CodingAgents# #LLMOps#